AEenrich (version 1.0.0)

enrich: Perform Adverse Event Enrichment Tests

Description

Adverse event (AE) enrichment analysis. Unlike the continuous gene expression data, AE data are counts. Therefore, AE data has many zeros and ties. We propose two enrichment tests. AEFisher is a modified Fisher's exact test based on pre-selected significant AEs, while AEKS is based on a modified Kolmogorov-Smirnov statistic.

Usage

enrich(
  df,
  dd.group,
  drug.case,
  drug.control = NULL,
  method = "aeks",
  n_iter = 1000,
  q.cut = 0.1,
  or.cut = 1.5,
  seed = NULL,
  verbose = FALSE
)

Arguments

df

a data.frame with 3 columns. The function allows two data types. One type (data type I) consists data on the report level, including ID, Drug type and AE name. The other type (data type II) consists of aggregated data, including drug type, AE name and Count. Data should be ordered as ID, Drug type, AE name in data type I and Drug type, AE name, Count in data type II.

dd.group

a data.frame with AE name and Group name. This data.frame have the group information for each individual AE.

drug.case

a character string for the target drug of interest.

drug.control

a character string for the reference drug. If NULL(default), all other drugs combined are the reference.

method

a character string specifying the method for the enrichment test. It must take "aeks" (default) or "aefisher"; "aeks" is the rank-based enrichment test, and "aefisher" is the modified Fisher enrichment test. See details described in the paper (see reference section of this document).

n_iter

an integer value specifying the number of iterations in aeks method or the number of permutations in aefisher.

q.cut

a numerical value specifying the significance cut for q value of AEs in aefisher.

or.cut

a numerical value specifying the significance cut for odds ratio of AEs in aefisher.

seed

a numeric seed for reproducible analysis.

verbose

logical, if TRUE, print iterations. If FALSE, silence printing to the console. Default is verbose = FALSE.

Value

A list containing 2 data.frames named Final_result and AE_info.

The Final_result data.frame contains the following columns:

• GROUP_NAME: AE group names

• ES: enrichment score

• p_value: p value of the enrichment test

• GROUP_SIZE: number of AEs per group

The AE_info in 'aeks' contains the following columns:

• AE_NAME: AE names

• Ratio: reporting rate

• p_value: p value for AE-drug association

The AE_info in 'aefisher' contains the following columns:

• AE_NAME: AE names

• OR: Odds ratio for AE-drug association

• p_value: p value for AE-drug association

References

Li, S. and Zhao, L. (2020). Adverse event enrichment tests using VAERS. arXiv:2007.02266.

Examples

# NOT RUN {
drug.case = 'FLUN'
drug.control = 'FLU'

# AEKS
## Input data using data Type I
KS_result1 = enrich(df = flu1, dd.group = group, drug.case = drug.case, 
                    drug.control = drug.control, method = 'aeks', n_iter = 10)
## Input data using data Type II
# }
# NOT RUN {
KS_result2 = enrich(df = flu2, dd.group = group, drug.case = drug.case, 
                    drug.control = drug.control, method = 'aeks', n_iter = 1000)

# AEFisher
fisher_result1 = enrich(df = flu1, dd.group = group, drug.case = drug.case, 
                        drug.control = drug.control, method = 'aefisher', 
                        n_iter = 1000, q.cut = 0.1, or.cut=1.5)
# }